PHP implements the recommendation system and personalized recommendation functions in the knowledge question and answer website.

PHP implements the recommendation system and personalized recommendation function in the knowledge question and answer website

With the rapid development of the Internet, the knowledge question and answer website has flourished and has now become an important way for users to obtain knowledge and solve problems. However, it is not easy for users to find content that interests them and suits them among many questions and answers. Therefore, in order to improve user experience and stickiness, the recommendation system has become a key function.

This article will introduce how to use PHP to implement the recommendation system and personalized recommendation function in the knowledge question and answer website. In the recommendation system, we can analyze and predict the user's preferences based on the user's interests and behavior records, and then recommend relevant questions and answers to the user. Below are some key steps and code examples.

Step 1: Collect user data
First, we need to collect user interest and behavior data for subsequent recommendations. This data can be obtained through user registration, login and browsing history. The following is a simple sample code for saving user behavior records to the database.

// 连接数据库
$conn = mysqli_connect('localhost', 'username', 'password', 'database');

// 获取用户ID
$userId = $_SESSION['userId'];

// 获取用户浏览记录
$questionId = $_GET['questionId'];
$answerId = $_GET['answerId'];

// 插入用户行为记录
$sql = "INSERT INTO user_behavior (user_id, question_id, answer_id, action_time) VALUES ('$userId', '$questionId', '$answerId', NOW())";
mysqli_query($conn, $sql);

Step 2: Calculate the user’s interest model
Then, we can calculate the user’s interest model by analyzing the user’s behavior records to understand the user’s interest preferences and recommend relevant content to them. Below is a simple example code for calculating a user's interest model.

// 计算用户的兴趣模型
function calculateUserInterest($userId) {
    // 连接数据库
    $conn = mysqli_connect('localhost', 'username', 'password', 'database');
    
    // 查询用户的浏览记录
    $sql = "SELECT question_id, answer_id FROM user_behavior WHERE user_id = '$userId'";
    $result = mysqli_query($conn, $sql);
    
    // 统计用户对每个问题的浏览次数
    $interestModel = array();
    while ($row = mysqli_fetch_assoc($result)) {
        $questionId = $row['question_id'];
        if (!isset($interestModel[$questionId])) {
            $interestModel[$questionId] = 0;
        }
        $interestModel[$questionId]++;
    }
    
    // 返回用户的兴趣模型
    return $interestModel;
}

// 示例调用
$userId = $_SESSION['userId'];
$interestModel = calculateUserInterest($userId);

Step 3: Make recommendations based on the user’s interest model
Finally, we can make personalized recommendations based on the user’s interest model. The recommendation algorithm can select content that best matches the user's interests for recommendation based on the user's interest preferences and the relevance of questions and answers in the community. The following is a simple sample code for implementing interest-based model recommendations.

// 根据用户的兴趣模型进行推荐
function recommendQuestions($interestModel) {
    // 连接数据库
    $conn = mysqli_connect('localhost', 'username', 'password', 'database');
    
    // 构建推荐查询语句
    $sql = "SELECT question_id, COUNT(*) AS score FROM user_behavior WHERE ";
    $conditions = array();
    foreach ($interestModel as $questionId => $interest) {
        $conditions[] = "question_id != '$questionId' AND (answer_id = '' OR answer_id NOT IN (SELECT answer_id FROM user_behavior WHERE user_id = '$userId' AND question_id = '$questionId'))";
    }
    $sql .= implode(" AND ", $conditions);
    $sql .= " GROUP BY question_id ORDER BY score DESC LIMIT 5";
    $result = mysqli_query($conn, $sql);
    
    // 返回推荐问题列表
    $recommendedQuestions = array();
    while ($row = mysqli_fetch_assoc($result)) {
        $recommendedQuestions[] = $row['question_id'];
    }
    
    // 返回推荐问题列表
    return $recommendedQuestions;
}

// 示例调用
$recommendedQuestions = recommendQuestions($interestModel);

Through the above steps, we can implement the recommendation system and personalized recommendation functions in the knowledge question and answer website. When users browse questions and answers, we can collect user behavior records in time and recommend relevant content to them by analyzing the user's interest model. This will not only improve user experience and stickiness, but also provide users with more personalized services. Of course, the above is just a simple example. In an actual recommendation system, more factors need to be considered, such as the user's login status, the popularity of questions and answers, etc., to provide more accurate and efficient recommendations.

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